Teleoperation for a ball-catching task with significant dynamics

NOTICE: this is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, VOL 21, ISSUE 4, May 2008 DOI: 10.1016/j.neunet.2008.04.002 QC 20110530
In this paper we present ongoing work on how to incorporate human motion models into the design of a high performance teleoperation platform. A short description of human motion models used for ball-catching is followed by a more detailed study of a teleoperation platform on which to conduct experiments. Also, a pilot study using minimum jerk theory to explain user input behavior in teleoperated catching is presented.

Bratt, MattiasKTH,Centrum för Autonoma System, CAS(Swepub:kth)u1frz8il (författare)
Christensen, HenrikKTH,Centrum för Autonoma System, CAS(Swepub:kth)u10yhaln (författare)
KTHCentrum för Autonoma System, CAS (creator_code:org_t)

Av författaren/redakt...: Smith, Christian; Bratt, Mattias; Christensen, Hen ...

Om ämnet

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.